Object-tracking systems and methods

ABSTRACT

A system and method for tracking, identifying, and labeling objects or features of interest, such as follicular units is provided. In some embodiments, tracking is accomplished using unique signature of the follicular unit and image stabilization techniques. According to some aspects pixel data of a region of interest in a first image is compared to pixel data of the regions of interest in a second image, and based on a result of the comparison of pixel data in the region of interest in the first and second images and the signature of the follicular unit, locating the follicular unit in the second image. In some embodiments the follicular unit is searched for in the direction of a motion vector.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/459,968 entitled “Object Tracking Systems and Methods”, filed Aug.14, 2014, which is a continuation of U.S. patent application Ser. No.12/240,724, entitled “Object Tracking Systems and Methods”, filed Sep.29, 2008, now U.S. Pat. No. 8,848,974.

FIELD

Embodiments disclosed herein relate generally to an object-trackingsystem, and more particularly, to a system and method for accuratelytracking hair follicles or other sites and features on a body surface.

BACKGROUND

A hair transplantation procedure typically involves harvesting donorhair grafts from a donor area, for example, the side and back fringeareas, of a patient's scalp, and implanting them in a bald area, orrecipient area. In the past, the harvested grafts were relatively large,between 3-5 mm. However, recent donor grafts may be as small as singlefollicular units. Follicular units (FUs) are naturally-occurringaggregates of one to five closely-spaced hair follicles that aredistributed randomly over the surface of the scalp.

The follicular units may be classified, or “typed,” based on the numberof hairs in the unit, and identified in shorthand as an “F1” (singlehair follicular unit), “F2” (two-hair follicular unit), etc. In somecases of multiple hair follicular units, the hairs may appear to emanatefrom a single follicle or point in the skin. In other cases, the hairsmay exit the skin slightly spaced from one another.

During a hair transplant procedure, certain locations should be avoidedfor harvesting or implanting hairs. For example, if a doctor alreadyused a site for harvesting or implanting hairs, the doctor may want toavoid using the same site for a subsequent procedure. Tracking deviceshave a difficult time tracking some of these sites.

A system is needed that may include a database of harvested andimplanted sites. A doctor may use the database to plan future harvestingand implantation. The system may track and record site information evenwhen the actual site cannot be seen, because the system can trackfeatures around the site, including follicular units, scalp features, orexternal markers.

A system is also needed that can track moving features. One way oftracking features is detecting their locations on a still by stillbasis. A system is needed that can improve tracking and does not need todetect feature locations on a still by still basis. Such a system mayutilize motion vectors of features or markers to improve tracking.

SUMMARY

Briefly, and in general terms, there is disclosed an object-trackingsystem. More particularly, there is disclosed an object tracking systemand method that track objects on a body surface that are not otherwiseeasily tracked.

In one aspect, a method for tracking a feature of interest, such asfollicular unit, involves identifying at least one marker in a firstimage, the first image including the feature of interest, such as afollicular unit; computing a signature of the feature of interest in thefirst image; identifying the at least one marker in a second image;computing a motion vector corresponding to a change in position of theat least one marker from the first image to the second image; and usingthe motion vector and the signature of the feature of interest in thefirst image to label the feature of interest in the second image.

In another aspect, there are at least three markers in the first image.Using three or more markers allows tracking both translational androtational movement. The at least one marker can embody one or more ofanother follicular unit, a mole, a scar, a freckle, a wrinkle, a bump,or a depression of the body surface. Additionally, a plurality ofmarkers can be included in the first image and identifying the at leastone marker in a first image can involve identifying a point cloud.

In one approach, a centroid of the point cloud is calculated. Computinga signature of the follicular unit in a first image can involveidentifying one or more of a length, type, caliber, emergence angle,area, shape, and color of the follicular unit. Further, in one specificapproach, at least one marker in the first image is labelled, and the atleast one marker is labelled in the second image with the same label asin the first image by searching for the at least one marker in thesecond image in the direction pointed to by the motion vector.

In one embodiment, additional motion vectors could be used. For example,a second motion vector is computed from the first image and the secondimage, and the second motion vector is used to label the follicular unitin the second image. The second vector can be computed using one or moreof the Gray-Coded Bit-Plane, optical flow, and block search imagestabilization techniques.

In various other aspects, an image-capture device is moved according tothe motion vector to keep the feature of interest, such as follicularunit, in a field of view of the image-capture device. Moreover,identifying the at least one marker can involve associating electronicidentifiers with the at least one marker. Also, any marker in the secondimage inconsistent with its location in the first image according to themotion vector is discarded from consideration.

Additionally, the motion vector can define a search region for locatingthe follicular unit or other feature of interest. Defining the searchregion involves analyzing a region along the motion vector and within apredetermined distance from the motion vector. The predetermineddistance from the motion vector can embody a cone having a tip at alocation of the marker in the first image, the tip having apredetermined tip angle, the cone extending in a direction of the motionvector and including an area within the predetermined tip angle.

In various specific approaches, the first and second images are dividedinto a plurality of first and second sub-images, and a plurality ofmotion vectors from the first and second sub-images are computed.Further, the follicular unit that has been tracked may be harvested froma donor area and may be implanted in a recipient area.

In another aspect, a method for tracking a feature of interest, such asa follicular unit, can involve receiving a first image of a body surfacecontaining follicular units and a second image of the same body surface;identifying a follicular unit in the first image; computing a signatureof the follicular unit in the first image; computing a motion vectorfrom the first image and the second image; and using the motion vectorand the signature of the follicular unit in the first image to label thefollicular unit in the second image. The motion vector could be computedwithout using any markers but rather from the image as a whole.Computing the signature of the follicular unit in the first image caninclude identifying one or more of a length, type, caliber, emergenceangle, area, shape, and color of the follicular unit.

In yet another aspect, a system for tracking a feature of interest, suchas a follicular unit, on a body surface includes an imaging device forcapturing at least two images, a first image including a first markerand a follicular unit; a signature identification component, wherein thesignature identification component identifies signature informationabout the follicular unit; a vectoring component, wherein the vectoringcomponent calculates a motion vector between a first image of the twoimages and a second image of the two images; and a tracking system forreceiving data corresponding to the motion vector, and labelling thefollicular unit based on the motion vector and the follicular unitsignature information. The tracking system can be further programmed tomove the imaging device based on the motion vector. The signatureidentification component, vectoring component, and tracking system canbe part of a single computer-program product. Moreover, the system fortracking a follicular unit or other feature of interest on a bodysurface can further embody a marker identification component. The markeridentification component may be a part of the signature identificationcomponent or may be a separate component or program. Also, one or moreof the signature identification component, the vectoring component, andthe tracking component can include a processor connected to at least oneof memory and the imaging device.

The imaging device can be a camera, and the system may be a roboticsystem further including a robotic arm. Also, the imaging device can beoperably connected to the robotic arm. It is also contemplated that asystem for tracking/labelling feature of interest, such as follicularunits, may include an interface adapted to receive an image datacontaining follicular units; and an image processor comprising one ormore modules for executing operations on the image data, the one or moremodules including instructions for: receiving a first image of a bodysurface containing follicular units and a second image of the same bodysurface; identifying a follicular unit in the first image; computing asignature of the follicular unit in the first image; computing a motionvector from the first image and the second image; and using the motionvector and the signature of the follicular unit in the first image tolabel the follicular unit in the second image.

In another aspect, a computer program tangibly embodied in acomputer-readable memory medium and including instructions that cause aprocessor of a computing device to execute a computer process fortracking a feature of interest, such as a follicular unit, comprisesidentifying at least one marker in a first image, the first imageincluding a follicular unit; computing a signature of the follicularunit in the first image; identifying the at least one marker in a secondimage; computing a motion vector corresponding to a change in positionof the at least one marker from the first image to the second image; andusing the motion vector and the signature of the follicular unit in afirst image to label the follicular unit in the second image. Further,it is contemplated that the processor is operatively associated with thememory. The computer process can also involve comparing first markerdata from the first image to first marker data from the second image andstoring a resulting vector in memory. The computer program can beoperatively associated with an image-capture device.

In still other aspects, a method for tracking a feature of interest (forexample, a follicular unit) on a body surface involves identifying atleast one marker in a first still image, the first still imagecontaining a feature of interest; computing a signature of the featureof interest in the first image; defining a frame of referencecorresponding to the at least one marker; locating the at least onemarker in a second still image; and detecting a position of the featureof interest in the second still image using the computed signature andupdating a translational component of the frame of reference based on aposition of the at least one marker in the second still image.

An approach can also include identifying at least three markers in afirst still image, and wherein detecting a position of the feature ofinterest in the second still image comprises updating both translationaland rotational components of the frame of reference. Further, the atleast one marker in the first still image could be identified by, forexample, analyzing at least one of a length, an area, a shape, a type,or a color of such marker.

Additionally, the at least one marker can be a follicular unit andidentifying the at least one marker further comprises analyzing at leastone of the emergence angle of the follicular unit from the body surfaceand the caliber of the hair. The still images can be video images of abody surface, the at least one marker is a follicular unit on the bodysurface, and the feature of interest is another follicular unit on thebody surface.

The frame of reference can be updated by using a point cloud approachand an imaging device can be moved according to an average shift of thepoint cloud. A feature of interest can be a bald spot and the method canfurther involve defining one or more hair implantation sites.

In another approach, a method for tracking a features of interest (suchas a follicular unit) on a body surface includes identifying at leastthree markers in a first still image, the first still image displaying afeature of interest; computing a signature of the feature of interest inthe first still image; tracking at least three first objects in thefirst still image; defining a frame of reference corresponding to thethree markers; determining whether the three markers are in a secondstill image; and in response to at least one of the three markers notbeing in the second still image, detecting a position of the feature ofinterest in the second still image using the computed signature of thefeature of interest and updating the frame of reference based on aposition of any of the at least three markers in the second still imageand any of the first objects in the second image. A sum of a combinationof markers and first objects used to detect the position of the featureof interest is at least three.

At least one of the at least three first objects can include afollicular unit, a mole, a scar, a freckle, a wrinkle, a bump, or adepression of the body surface. Detecting the position of the feature ofinterest can further involve computing a motion vector from the firststill image and the second still image and using the motion vector toupdate the frame of reference and to locate at least one of the at leastthree markers. At least one first object in the first still image isidentified and the identified first object is located in the secondstill image, and computing the motion vector includes identifying achange in position of the identified first object from the first imageto the second image.

It is also contemplated that a system for tracking a feature of intereston a body surface can include an interface adapted to receive aplurality of images from an image capture device, at least a first imagecomprising a feature of interest; a signature identification component,wherein the signature identification component identifies signatureinformation about the feature of interest and detects at least onemarker in the first image; and a marker referencing system. Thereferencing system defines a frame of reference corresponding to the atleast one marker, determines whether the at least one marker is in asecond image, and adjusts the frame of reference corresponding to achange in position of the at least one marker from the first image tothe second image to locate the feature of interest. The signatureidentification component and the marker referencing system can be partof a single computer program product. Also, the signature identificationcomponent can include a processor connected to memory, and the processorassociates an electronic identifier with the at least one marker andstores the electronic identifier in the memory. Moreover, the markerreferencing system can include a processor connected to memory, and theprocessor compares a location of each of the at least one marker in thefirst image to a location of each of the at least one marker in a secondimage, calculates a corresponding change in a frame of reference, andstores a result of the calculation in the memory. The system may furthercomprise a vectoring component. The system can be a robotic systemfurther including a robotic arm.

Other features and advantages will become apparent from the followingdetailed description, taken in conjunction with the accompanyingdrawings, which illustrate by way of example, the features of thevarious embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated by way of example and not limitation in thefigures of the accompanying drawings. In the drawings, identicalreference numbers identify similar elements or acts. The sizes andrelative positions of elements in the drawings are not necessarily drawnto scale. For example, the shapes of various elements and angles are notdrawn to scale, and some of these elements are arbitrarily enlarged andpositioned to improve drawing legibility. Further, the particular shapesof the elements as drawn, are not intended to convey any informationregarding the actual shape of the particular elements, and have beensolely selected for ease of recognition in the drawings.

FIG. 1 illustrates a kinematic “frame”;

FIG. 2 illustrates updating a frame;

FIG. 3 illustrates harvesting planning using registration markers;

FIG. 4 illustrates implantation planning using registration markers;

FIG. 5 illustrates updating a frame after losing a registration pattern;

FIG. 6 illustrates updating a frame with points other than the originalregistration markers;

FIG. 7 illustrates updating a frame using follicular units on apatient's scalp; and

FIG. 8 illustrates a follicular unit cloud algorithm.

FIGS. 9A and 9B illustrate point-cloud tracking;

FIG. 10 illustrates motion vector calculation;

FIG. 11 illustrates follicular unit labelling;

FIG. 12 also illustrates follicular unit labelling and associatedissues;

FIGS. 13A and 13B illustrate a follicular unit labelling/trackingprocess, and a follicular unit labelling/tracking process using a motionvector, respectively;

FIG. 14 illustrates defining a search region;

FIG. 15 illustrates one exemplary image-stabilization process;

FIG. 16 illustrates an image comparison;

FIG. 17 illustrates an image stabilization with multiple vectorscalculated; and

FIG. 18 illustrates an exemplary system that tracks objects.

DETAILED DESCRIPTION

The various embodiments described below are provided by way ofillustration only and should not be construed to limit the claimedinvention. Those skilled in the art will readily recognize variousmodifications and changes that may be made to the disclosed embodimentswithout departing from the scope of the claimed invention. By way ofnon-limiting example, it will be appreciated by those skilled in the artthat particular features or characteristics described in reference toone figure or embodiment may be combined as suitable with features orcharacteristics described in another figure or embodiment. Further,those skilled in the art will recognize that the devices, systems, andmethods disclosed herein are not limited to one field, such as hairrestoration, but may be applied to any number of fields that requireobjects to be tracked.

The described systems and methods of the present disclosure are usefulin any application that requires tracking of individual objects viaimage guidance. They are also useful in any application where it isdesired to register a set of visual markers to create a reference frameand then track an object by using surrounding objects to update thereference frame, rather than tracking the object directly. For example,the concept of using the output of image stabilization algorithms toguide or steer an object tracker could find its way into a wide range ofmedical and non-medical application, such as image guided roboticprocedures involving fluoroscopy or x-ray imaging where fiducials areinserted into the anatomy, or video surveillance applications thatinclude pan-tilt-zoom cameras in conjunction with analytics to trackmoving objects through a dynamically changing scene.

The systems and methods of the present disclosure are especially usefulin hair transplantation procedures, including automated orcomputer-controlled hair harvesting and implantation. Therefore, variousexamples and embodiments described herein will use follicular units orhairs simply as one example of the application of the present disclosurefor purposes of describing some embodiments with the understanding thatit represents more broadly all other appropriate applications.

It should be understood that the exemplary methods described herein areespecially suited for use with a robotic system for treatment planning,hair harvesting and/or implanting. However, they are not limited by anymeans to the robotic applications; instead the described methods may beapplied to the manual procedures conducted by a human with a hand-helddevice that could be, for example, operably connected to the computerprocessor and imaging system. Manual, partially-, and fully-automatedsystems are also within the scope of the present invention.

When performing medical operations on a patient's skin or scalp, certainareas need to be tracked, so that a doctor or, for example, a roboticmechanism in case of the automated procedures, may return to the area,or avoid the area, at a later time. These areas may be tracked byviewing images of the area and identifying features on or around thearea. The identifying features are registered by recording data aboutthe features in a database, memory, or other data storage medium. Theidentifying features may be accessed at a later time and compared withstill images, or “stills,” to determine whether a region shown in thestill is the same as the region containing the identifiers. “Stills” maybe single images of a video feed, photographs, or other fixedrepresentations of the patient's skin.

One method of registering an area or feature involves identifying a setof markers in a specific pattern in a still that could be recognized inthe future. The markers are non-collinear, so they define a 3-D frame.Although a minimum of three markers are desirable to define a 3-D frame,more markers may be used. These markers in the still are called a“registration pattern” and can be recognized in subsequent stills. The3-D frame, consisting of coordinates x, y, z, and rotation coordinatesRx, Ry, and Rz, is called a “patient frame.” In each successive stillwhere the registration pattern is completely visible and identifiable,the patient frame is simply moved with the updated registration pattern.Coordinates x, y, and z may be referred to as the translation of thepatient frame. Coordinates Rx, Ry, and Rz may be referred to as theorientation of the patient frame. For clarity, the term “frame” isdefined in this specification as a location and an orientation definedfrom a known point of reference. A single section of a video or photo isreferred to in this specification as a “still” or an image. Although astill may be referred to as a “frame,” this specification only uses theword “frame” as defined above in its kinematic sense, and the word“still” to describe a single image of a video feed, to avoid confusion.

FIG. 1 illustrates an example of a kinematic frame. In this exemplaryembodiment, a robotic arm 103 is attached to a fixed base 101 having acenter 104. The arm 103 holds and positions a tool 102. The tool has aframe, labelled x, y, z, and the location and rotation of the tool 102is known with respect to the center 104 of the base. This location andorientation may be stored electronically. If the relationship betweenthe tool and the center 104 of the base changes, the frame may beupdated electronically.

After the registration pattern is identified and electronically saved,the location and orientation of the frame may be updated by analyzingthe position of the markers in a subsequent still. FIG. 2 illustratesupdating a frame based on a change in position of markers. In a firststill, still N, a three-dimensional frame is defined by the location ofmarkers 201, 202, and 203. The markers (also called “fiducials”) may be,for example, physical markers or anatomical landmarks on a patient'sskin or body surface, such as a follicular unit or hair, a mole, a scar,a freckle, a wrinkle, a bump, or a depression of the body surface. Themarkers may also be objects placed on or affixed to the patient's skin,sometimes called external fiducials. A fiducial is an object in a fieldof view of an imaging device that acts as a reference. It could beanatomical landmark, an external marker, or any combination of theabove. In still N−1, the markers have moved down and to the right in thestill. Another object, 204, has appeared in a location similar to themarker 201 in still N. However, the object 204 would not be confusedwith marker 201, because the marker 201 is identified with respect tomarkers 202 and 203. The frame is updated in a database or memory torecognize that the patient has moved, and that object 204 is not marker201.

In some exemplary applications, the registration pattern may be used forreal-time hair harvesting and implantation planning, as shown in FIGS. 3and 4, respectively. In FIG. 3, markers 301, 302, 303, and 304 define apatient frame. The location of each follicular unit 305 on a patient'sskin 306 may be mapped with respect to the patient frame. The portion ofthe patient's body surface that is used to harvest follicular units isthe “donor area” 307. The donor area's follicular unit density may becalculated by counting the follicular units in the still. The countingof the follicular units may be accomplished, for example, as describedin the commonly assigned patent publication WO 2008/024955 which isincorporated herein by reference. A physician may determine how manyfollicular units per cm² to extract from the donor area and to inputsuch information into the harvesting planning system. The harvestingplanning system in turn determines which follicles to extract based onthe number of follicular units and the number of desired follicularunits per cm² to be extracted. Other factors may also be used todetermine which follicular units to extract, including follicular unitcharacteristics and scalp/body surface characteristics.

Likewise, a similar methodology may be used during hair implantation.FIG. 4 illustrates an exemplary implantation method. The registrationpattern comprises, for example, markers 401, 402, 403, and 404. Themarkers define the recipient area 407, for example, on the scalp 406. Aplanning system receives inputs indicating the number of follicularunits to implant, and the implantation locations 409. Exemplaryfollicular units 408 and 405 may be analyzed to determine a follicularunit density, baldness pattern, or other criteria for implantingfollicular units. The bald area on the body surface may be a feature ofinterest that could be tracked and labelled to create implantation sitesaccording to the present disclosure.

A patient may move during a scalp analysis, harvesting, or implantation.The monitoring system can track each follicular unit and other areas ofthe scalp by comparing a still with a previous still, and updating thepatient's frame to correspond with the changed position of the markersfrom one still to the next.

However, sometimes one or more markers will not appear in a subsequentstill, because the patient has moved or turned so that the markers areoutside the still. Also, objects or blood may obscure or block one ormore markers. FIG. 5 illustrates a patient's moving the markers out ofthe still. Markers 501, 502, and 503 define a patient's frame on bodysurface 500. In still N, all markers 501, 502, and 503 are visible. Anadditional marker 504 is also visible and identified, and its positionmay be recorded with respect to the patient's frame formed from markers501, 502 and 503. Still N−1 shows how a patient has moved up and rotatedclockwise with respect to the still. Consequently, only marker 504 isvisible in still N−1. Since the location of marker 504 is known withrespect to the patient frame, the patient frame may be updatedcorresponding to the change in position of marker 504. However, in thisexample marker 504 can only be used to update coordinates x, y, and z.Marker 504, alone, provides no data for updating coordinates Rx, Ry, andRz, corresponding to the rotation of the patient's frame around each ofthe axes x, y, and z. Thus, marker 504 updates the translation of thepatient frame but not the orientation of the patient frame.

At least three non-collinear points should be visible in both still Nand still N−1 in order to update both the translation and orientation ofa patient frame. FIG. 6 shows a patient frame having three markers 601,602, and 603 that define the patient frame, and four additional markers604, 605, 606, and 607 that are identified by the system. In still N,all seven markers 601-607 are visible. In still N−1, the patient movedand rotated, and markers 601, 602, and 603 are no longer visible in thestill. However, the system can use, for example, markers 604-606 thatcontinue to stay in the field-of-view (FOV) after the patient's movementto update the patient's frame, including the orientation of the frame.As markers 604-606 are assumed to be fixed in the patient's frame, theframe may be moved such that those markers will have the same patientframe coordinates in each still.

Although external fiducials or markers may be used to define a patientframe, natural occurrences on the body surface may also be used. One ofthe most common features or objects on a body surface is a follicularunit comprising a certain number of hairs. Hairs are uniquely suited asmarkers in those applications involving hair transplantation, becausethe hairs are already tracked by the system for purposes of analysis,harvesting, and implantation. A center-of-mass, or centroid, of eachfollicular unit may be used as a marker. The movement of each centroidis tracked between stills, and the movement of one or more follicularunits may be analyzed to update a patient frame. For example, iffollicular units all move left by 1 mm, one may deduce that the patientframe has moved left by 1 mm. This principle works in all six degrees offreedom even if all registration markers defining a patient frame arelost from still to still, provided at least three follicular units arestill visible in the FOV, as shown in the following example.

FIG. 7 illustrates updating a patient frame with follicular units. Instill N, markers 701, 702, and 703 are used to define a patient frame.Follicular units 704, 705, 706, and 707 are tracked for analysis,harvesting, and implantation purposes. Still N−1 shows markers 701-703outside the still, which may occur, for example, when the body surfacerotates. Since the locations of 704-707 are known with respect to thepatient frame, any three follicular units of 704-707 may be used toupdate the patient frame, including translation and orientation.

When hairs or other registration markers are used to define or update apatient frame, the hairs may be tracked by any method, including using apoint cloud algorithm. B. K. P. Horn, “Closed Form solution of AbsoluteOrientation Using Unit Quaturnians,” J. Opt. Soc. AMA/Vol. 4 Apr. 1987,which is hereby incorporated by reference, discloses one such algorithm(“Horn algorithm”).

Point cloud algorithms that estimate a rigid-body transformation betweentwo point clouds only work robustly if the same set of points is used inboth stills. A rigid-body transformation is one that preserves the shapeof objects it acts on. Used in this context, a point-cloud undergoing arigid-body transformation will have its shape preserved. Therefore, acircular-looking point cloud remains circular, a hexagonal looking pointcloud remains hexagonal, etc. When using a point cloud algorithm with abody surface having many hairs, the algorithm will be most accurate ifthe same follicular units are used in each point cloud, and if morefollicular units are used to generate the cloud. It is also importantthat the same follicular units comprise the point cloud in each still.

FIG. 8 illustrates a problem associated with the typical “Hornalgorithm” that may arise when the patient moves and propercorrespondence of points is not maintained from still to still. In stillN, follicular units 803-809 may be used to generate a point cloud.However, in still N−1, the patient has moved, and follicular units 808and 809 are no longer visible in the still. Also, follicular units 801and 802 have entered the still. Since the same number of follicularunits exists in each still, a system may mistakenly generate a pointcloud using the seven follicular units in still N and associate it withthe seven follicular units in still N−1. Since the follicular units ineach still are different, the patient frame would be incorrectlyupdated.

To avoid the above problem, the system according to the presentdisclosure analyzes characteristics of the follicular units (or otherappropriate features) to determine if the same follicular units exist ineach still. Only the follicular units that exist in both stills are usedto generate a point cloud for defining and updating a patient frame totrack objects and locations on the body surface. Characteristics thatmay be used to identify follicular units, hairs, or other featuresinclude but are not limited to type, caliber, length, emergence angle,area, shape, color, and/or any combination of the above which woulddefine the unique “signature” of such follicular unit or other featureused. Any other detectable characteristics or “tags” may be used toidentify the follicular unit, hair, or feature.

The above-described “point cloud”-based updating of the patient frame isespecially useful in implantation automation and planning for use in therobotic hair implantation system. The number of follicular units forimplanting (N) is input into the system. The image processor locates anybald spots based on the images of a particular body surface using aclustering algorithm, and then generates N implant sites located withinthe identified bald spots. The patient frame may be formed from thesurrounding hairs and possibly any viewable external markers, and eachimplant site is defined with respect to the patient frame. As theimplanting tool (located on the robotic arm in the robotic systems, oroperated by the human) moves from one site to another implanting at eachdefined location, the image processor may update the position of thenext implant site in the set based on the updated patient frame.

When no hairs are visible on a body surface, or the visible hairs areineffective for identification and tracking by the system, for example,when the patient is bald or has a body surface which is extremelysparse, consisting mostly of miniaturized or “wispy” hair, features ofthe body surface may be used to track movement instead of hairs orfollicular units. In these situations, the system and method accordingto the present disclosure uses the body surface, such as human scalp,which is “trackable”, since it is not uniform and it containsinformation. For example, according to one embodiment of the methoddescribed herein, the system may compare a series of pixels orregions-of-interest (ROI) between one still (still N) and the next(still N−1). Each set of stills shows a translation movement of the bodysurface, and many sets of stills may be aggregated to show a singleshift of the scalp, or other relevant body portion. Furthermore, inother embodiments, moles, texturing, blood, and other features andobjects may be used in the comparison and to update the patient frame ona still to still basis. Any readily identifiable object or feature canbe used as a surrogate marker, and then its tracked position may be usedas input into the previously described registration algorithm.

Once a frame is registered, the frame and the features identified in theframe may be saved electronically, or by any other known method. Anytime a new still is presented to the system, a pattern-recognitionalgorithm determines whether a pattern in the still corresponds to aregistered and saved pattern. If it does, any identified feature isrecognized and identified in the new still, and the frame is updated.

The methods disclosed herein have another useful application duringpatient treatment such as during hair harvesting or implantationprocedure, for example. Sometimes, after the image processor located andidentified a particular follicular unit that is scheduled to beharvested, such follicular unit may be temporarily lost and disappearfrom the view, for example, due to the bleeding in the area, or due tothe physician applying a Q-tip to the area and obscuring the follicularunit of interest. Without the present invention, in those circumstances,there will be a substantial delay in the procedure as the follicularunit of interest would have to be located and identified again. However,the ideas disclosed herein allow the system to proceed with thescheduled harvesting as the patient frame and the position of thefollicular unit at issue is saved in the system and could be quicklyrecovered. Once the system has been positioned over the originalregistration pattern, a pattern-recognition algorithm recognizes thatsame pattern, and re-registers the frame. Because all other points areheld with respect to patient frame coordinates, simply reregistering theframe causes all other previously known points to be instantly knownagain.

According to another aspect, a method of labelling or tracking hairs,follicular units, or other features of interest or objects from onestill to the next involves calculating a motion vector. One method oftracking/labelling objects or features of interest, such as follicularunits using a motion vector is illustrated in FIGS. 9A and 9B. FIG. 9Ashows a body surface, such as a scalp 900, with four follicular units901-904 which could be used as markers or fiducials. A camera 905 or anyother image capture device records images of the scalp. Still N shows afirst image including follicular units 901-904. Each follicular unit hasa center-of-mass or “centroid,” the collection of these follicular unitsis deemed a “point-cloud” and the point-cloud itself has a centroid. Thesystem according to the present disclosure calculates the centroids906-909 of each follicular unit comprising a point cloud. A centroid 910of the point cloud is calculated based on the centroids 906-909 of theindividual follicular units 901-904 that comprise the point cloud. Thesystem includes an image processor that may be programmed and includesone or more components to perform background subtraction, segmentationand noise filtering of the image, for example, as described in thecommonly owned Patent Publication WO 2008/024955.

Still N−1 shows a change in location of the follicular units 901-904corresponding to a change in position of a patient, for example. Thecentroid 910 of the point cloud is calculated and compared to thelocation in still N. The system calculates a motion vector 911 of thepoint cloud corresponding to the change in position of the centroid 910from still N to still N−1.

However, problems may arise when any of the follicular units 901-904 areobscured due to imaging noise, blood, or otherwise removed from stills.The system may confuse similarly located follicular units as the absentfollicular unit or may generate a point cloud centroid that does notcorrespond to the centroid 910 corresponding to all four follicularunits 901-904. One solution is to employ digital image stabilization tokeep the same follicular units or other features or objects insubsequent stills.

FIG. 10 illustrates a process of calculating a motion vector. Stills 0and 1 contain four follicular units 1001-1004. The follicular units havechanged location between still N and still N−1, which may correspond topatient movement, for example. The system compares stills 0 and 1 anduses a motion vector algorithm to calculate a motion vector 1005 of thepatient's body surface.

While FIGS. 9-10 illustrate calculating motion vectors in two-dimensionswhere “dx” is the horizontal motion component and “dy” is thecorresponding vertical motion component, the system may also calculate athree-dimensional motion vector. One method for calculating a 3D motionvector may include calculating an in-plane rotation theta. Anothermethod includes using stereo geometry to acquire two imagessimultaneously from different angles. The relationship between theimages is known with a high degree of precision. The stereo images maythen be analyzed with the motion vector algorithm to obtain either a 2Dor 3D motion vector. Once the system calculates a motion vector, thevector may be used to move an imaging device to keep an object orfeature of interest in a subsequent still.

It is particularly important to maintain the same follicular units insubsequent stills during harvesting and implanting. An imaging device isused to generate stills of a body surface. The imaging device may beheld by hand, by a robotic arm, or by any other mechanism. Of course,various image capture devices (or imaging devices) could be used withany of the embodiments of the systems and methods described herein. Forexample, the imaging device may be one or more cameras, such as anycommercially available cameras. Or, the imaging device could be a videorecording device (such as a camcorder). While it is preferred that theimaging device be a digital device, it is not necessary. It could be,for example, an analog TV camera that acquires an initial image which isthen digitized into a digital image.

The physician examines the stills to determine which follicular unitswill be harvested and which locations will receive follicular unitsduring implantation. During this process, it is helpful if body surfacethat is actually beneath the imaging device is the same as the stillbeing examined by the physician. A physician can finish examining astill and immediately access the area in the still if the imaging deviceis kept in place with respect to the patient's body surface. This may beaccomplished by continually feeding stills into the stabilizing system,analyzing motion vectors corresponding to patient movement, and movingthe imaging device to correspond to the motion vectors. If the imagingdevice is supported by a robot arm, the robot arm location may beadjusted corresponding to the motion vectors.

One exemplary application is in improving the robustness of thefollicular units tracking/labelling, for example, during automated hairharvesting procedure. During such a procedure, the system has to trackthe coordinates of individual follicular units in order to orient theharvesting tool and the mechanism operating the harvesting tool inpreparation for hair harvesting. This is accomplished by assigning aunique label to each object in a still image, as shown in FIG. 11. Instill N, follicular units 1101-1103 are recognized, their unique“signature” is identified, and they are labelled accordingly.

In preferred embodiments, the system analyzes characteristics of thefollicular units (or other desired objects) to help identify the samefollicular units in each subsequent still. Characteristics that may beused to identify follicular units, hairs, or other features include butare not limited to type, caliber, length, emergence angle, area, mass,color, and/or any combination of the above which would define the unique“signature” of such follicular unit or other feature used. Any otherdetectable characteristics or “tags” may be used as appropriate to theparticular application. In still N−1, the same objects are located andlabelled with the same labels. This may be done by searching in thenearby vicinity for the nearest object. This process may be made morerobust and accurate by using both the determined “signature” of thefollicular unit and a motion vector of the patient between still N andstill N−1 to aid in locating the same objects in the two stills. Thoseskilled in the art will appreciate that while one follicular units orhair may be the object of interest for tracking, other follicular unitsin the neighbourhood may serve as the markers, and may be used tocalculate the motion vector.

In certain situations, the system may become confused, for example, ifnew follicular units or other features are introduced into a subsequentframe near a previously-identified follicular unit. FIG. 12 illustratesseven follicular units 1201-1207 in still N. Still N−1 contains ninefollicular units, including 1201-1207 from still N, plus new follicularunits 1208 and 1209. Follicular units 1208 and 1209 were outside thestill's view area in still N, but if the patient moves relative to theimage capture device, they appear in still N−1. If the system analyzesand identifies follicular units based only on vicinity to the locationof follicular units in a previous still, it may confuse follicular units1208 and 1209 with 1206 and 1203.

A preferred solution for providing a more robust identification offollicular units involves incorporating a digital image stabilizer inthe tracking loop and using it in combination with the unique signatureinformation of the relevant follicular unit, as shown in FIGS. 13A and13B. FIG. 13A depicts a method of labelling follicular units or otherfeatures extracted from grayscale stills, and identifying follicularunits in a still N−1 by finding the closest object to the follicularunit previously identified in still N. In this process, grayscale stillsmay be converted to the segmented stills (for example, binary stills)consisting purely of foreground objects (for example, follicular units).Background components are removed from the still using image processingtechniques well-known to those skilled in the art.

FIG. 13B illustrates the same process, but improved with imagestabilization methodology and signature information. The system analyzesgrayscale stills N and N−1 and determines follicular unit ID (includingits signature information). The stills are compared to calculate amotion vector, the stills are segmented, and the follicular unitsignature and the motion vector is applied to the frame N−1 to searchfor an object corresponding to a follicular unit of the first frame bysearching in a direction of the motion vector. Using this extrainformation about the motion-induced “flow” of objects in the image, onecan discard completely follicular units 1208 and 1209 shown in FIG. 12because the motion-induced flow is towards the lower-left corner of theimage and the signature of the follicular unit 1209 does not correspondto the signature of the follicular unit 1206. This is because,follicular unit 1206 is an F2 containing two hair follicles whilefollicular unit 1209 is a single-hair F1.

By applying the image stabilization analysis, the chance ofmis-labelling is substantially reduced, because the system searches inthe direction of the movement vector rather than in the vicinity of thefollicular unit in the first still. FIG. 14 illustrates an example ofdefining a search field based on a general direction pointed to by themeasured motion vector. Motion vector 1401 may be calculated asdisclosed above. Some leeway may be desired in setting the trackingparameters, which is accomplished, for example, by specifying an angle,θ, that is used to define a search area on either side of the movementvector where the system searches for the object.

Many digital image stabilization algorithms may work with the methoddescribed herein to result in a robust system for identifying featuresor follicular units. One such algorithm which is incorporated herein byreference in its entirety is an approach using a gray-coded bit-plane(GCBP) as described in S. Ko, S. Lee, S. Jeon, and E. Kang. Fast digitalimage stabilizer based on gray-coded bit-plane matching. IEEETransactions on Consumer Electronics, vol. 45, no. 3, pp. 598-603,August 1999. Implementation of the gray-coded bit-plane approach isillustrated in FIG. 15. First, an input image is received from animaging device. The image is translated into a gray-coded bit-plane. Insome embodiments, a region of interest 1601 may be spliced from thegray-coded bit-plane 1602 to reduce the search area and strain oncomputing components, as shown in FIG. 16. A search region 1603 that issmaller than the GCBP still but larger than the region of interest 1601may also be defined. The gray-coded bit plane of the region of interestis compared with the gray-coded bit plane of a previous image.

In contrast to the point-cloud approach of calculating motion vectors,where the calculations to deduce overall movement of the cloud takesplace in “object space” (requires knowledge of the centroids of thefollicular units or other markers), the GCBP image stabilizationapproaches mentioned above operate purely on the pixel intensity data,without any a priori knowledge of where particular features, forexample, follicular units are located, or even if there are follicularunits in the scene. This has the strong benefit of providing acompletely independent measure of motion, which is useful in trackingbecause extraction of follicular unit coordinates can be a noisy measuredue to the dynamic nature of the scene being imaged.

There are numerous other digital image stabilization algorithms that maybe used instead of the gray-coded bit plane. Some of the examples ofsuch stabilization algorithms include but are not limited to the“optical flow” technique described in Jean-Yves Bouquet. PyramidalImplementation of the Lucas Kanade Feature Tracker. Intel Corporation; a“block search” technique, as described in Vella F et al. “Robust digitalimage stabilization algorithm using block motion vector.” ConsumerElectronics, ICCE, p. 234-235, 2002 or in Sun Yi et al. “Real-timedigital image stabilization algorithm on PC.” Electronic Imaging andMultimedia Technology III, SPIE Vol. 4925, p. 510-513.

“Optical flow” estimation algorithms compute a field of vectors thattogether approximate the perceived motion of objects within an image, orseries of images (as in video). A vector is calculated from a certainpoint within the image or image(s) that represents the localized motionnearby that point. In the Bouguet article, cited above, the optical flowcalculations are estimated using a pyramidal and hierarchical searchmethod. An initial guess of the localized motion is estimated from a lowresolution version of the image(s), which is successively refined as thealgorithm traverses “up the pyramid” utilizing higher-resolutionversions of the image(s).

“Block search” methods borrow techniques commonly employed in videocompression standards like MPEG. As described in Vella (cited above),the key change between block search employed in video compression andthat used in digital image stabilization is that the image(s) are firstsplit into foreground and background components. Foreground andbackground are in turn split into sections where each section isassigned a separate weight. A motion vector is computed for each sectionand given its weight. The weighted motion vectors are combined to form asingle vector for background and foreground. A heuristic is then used tochoose which of the two motion vectors to use for the stabilization.

Finally, the method described herein for further improvement of therobustness of the labelling/tracking mechanism may employ both computinga motion vector based on one or more objects or markers in the image andin addition also computing a motion vector from the image itself as inthe above-mentioned articles, so that the total calculation is based onboth motions vectors.

Further, in one implementation of this system, the stills may be splitinto multiple images to generate multiple vectors, as illustrated inFIG. 17. Still N is divided, for example, into four sections, Q1-Q4.Each section 1701 corresponds to at least one different feature orfollicular unit. A subsequent still N−1 is also divided intocorresponding sections and may be further spliced into correspondingsearch areas 1702 and regions of interest 1703. The sub-sections arecompared and a motion vector may be obtained for each sub-section. Theresulting motion vectors may indicate rotational movement of the patientwith the increased accuracy.

In any of the methods for calculating a motion vector, a still may beeither a reference still, or a changeable still. For example, areference still image may be taken of a region of skin on the patient'sbody. Then, each subsequent still image may be compared to the referencestill image to determine a motion vector. The motion vector may be usedto direct an imaging device to move in the direction of the vector sothat the image in the imaging device is the same as the reference still.Alternatively, the system may compare a second still to a first still,calculate a movement vector, and move an imaging device according to themovement vector. A subsequent third still may then be compared to thesecond still, and the process repeated. In this case, each subsequentstill is compared to the previous still, rather than to a referencestill.

Although the motion vector may be used to move an imaging device, it mayalso be used as tracking data or any other purpose. For example, thesystem may record patient movement by recording movement vectors for apredetermined time period or a predetermined number of stills. Thesystem may also be programmed to move an imaging device only when acertain amount of movement, or a certain movement vector magnitude hasbeen reached. For example, the system may be programmed to make sure apredetermined follicular unit, location, or point is always within thefield of view of the imaging device, or within a predetermined area inthe field of view of the imaging device.

Another exemplary application of the image stabilization techniqueinvolves stabilizing the images during the automated hair harvestingprocess. During such automated hair harvesting, all follicular units ina given image may be sorted based on a scoring system, so that aparticular follicular unit to be harvested next can be selected.Typically, there will be a pause during the procedure when the operatorlooks, for example, at the screen of the computer or other appropriatedisplay to confirm that the image processor correctly chosen the bestfollicular unit to harvest according to the selected criteria. Duringthis time when an operator is inspecting a static still image of thebody surface, a patient may move, therefore, it is desirable to keep thevideo stabilized during this period of time.

Importantly, once the user confirms the choice (or even selects adifferent one), the system needs to ensure that the selected follicularunit is still in the live still. This is achieved through employing theresults of digital image stabilization according to the presentdisclosure. In some embodiments, the 3D motion vector output from thestereo image stabilizer is negated and passed, for the example, to therobotic arm (in the robotically operated systems). This has an effect ofnegating the patient motion, or moving one or more cameras that could bemounted on the robotic arm, such that the same field of view will beimaged in subsequent image acquisitions.

Yet another exemplary application of the image stabilization techniqueinvolves using the output of the stabilizer to guide the robotic systemduring hair implantation. Exemplary robotic systems and methods for hairtransplantation and treatment planning are described in the commonlyowned U.S. Publication 2007/0078466 and U.S. patent application Ser. No.12/133,159, which are incorporated herein by reference in theirentirety. When any of the above-mentioned systems is used, follicularunits may be implanted in the locations prescribed by the selectedtreatment plan.

Typically, the hair recipient areas are sparse in terms of the number offollicular units that an imaging system can reliably track. That is whythere is a need to keep track of the motion of the robot or patientmotion through some other means aside from tracking follicular units.This can be achieved by tracking the motion of the body surface (e.g.scalp) via the image stabilizer. The treatment plan can be registeredwith the recipient area using external fiducials. This registrationestablished a coordinate frame of reference which has to be updated asthe robot and patient move during the treatment. The coordinate frame ofreference (coordinate system) may be updated by the visual system byidentifying the locations of the fiducials. However, even if thefiducials cannot be seen sometimes, as previously explained, one cancontinue to update the coordinate system in a delta fashion by addingthe measured motion vector on a still by still basis.

An exemplary system for tracking a follicular unit, a marker, or anotherobject is illustrated in FIG. 18. In some embodiments, the system mayinclude an imaging device 1800, a signature identification component1801, a vectoring component 1802, and a tracking system 1803. Theimaging device 1800 may include a camera such as a video camera.Alternatively, any other device capable of capturing an image may beused. Preferably, the imaging device is a digital imaging device. Inother embodiments, an imaging device may be provided separately and notincluded in the system. In those embodiments, an interface may beprovided that allows various other components or modules of the system,such as signature identification component, to interact with theseparate imaging device.

The imaging device 1800 interacts with the signature identificationcomponent 1801 to identify a follicular unit, a marker, or anotherobject. The signature identification component 1801 may be a softwareprogram with code stored on a portable disk, a hard disk, or othermemory. The code may interact with a computer system 1810 including, forexample, a processor 1807 to identify distinguishing characteristics ofa follicular unit, a marker, or another object. Alternatively, thesignature identification component 1801 may be embodied in a hardwareplatform, for example, Field-Programmable Gate Array (“FPGA”) orApplication-Specific Integrated Circuit (“ASIC”), and interact with acomputer system 1810 or a processor 1807. The computer system 1810 orprocessor 1807 interacts with the imaging device 1800 via an interface1811. The interface may include hardware ports, cables, leads, and otherdata transmission means, or it may comprise a computer program.

As discussed above, type, caliber, length, emergence angle, area, color,and/or any combination of the above may be used to define the unique“signature” of a follicular unit or other feature or object. Any otherdetectable characteristics or “tags” may be used as appropriate to theparticular application. The signature identification program 1801analyzes the image from the imaging device, identifies characteristicsof a follicular unit or other feature, and electronically tags thefollicular unit or other feature with an electronic signature.

When two or more stills are available, in certain embodiments accordingto the methods described herein the vectoring component 1802 may be usedto assist the tracking system in tracking a follicular unit or otherfeature. The vectoring component 1802 may be a software program orhardware that interacts with a computer system 1810 including aprocessor 1807 to analyze movement of the follicular unit. The vectoringcomponent 1802 receives location information for a known feature. Theknown feature may have been identified by the signature identificationunit 1801, for example. The location information is based on thelocation of the known feature in the stills. The vectoring component1802 uses the location information to calculate a vector of the knownfeature.

Information from the vectoring component 1802 may be used to track afollicular unit or other feature using the tracking system 1803. Thetracking system 1803 may include a robotic base 1808 and arm 1809, amovement adjustment mechanism in the imaging device, code for presentingan image or a portion of an image on a screen, or any other mechanismfor tracking an object between multiple stills. If the tracking unitcomprises a robot arm 1809, the imaging device 1800 and a harvesting oran implantation tool 1806 may be located at the end of the robot arm1809. The vector from the vectoring component 1802 may be used to movethe tracking system 1803 in the movement direction of the stills to keepthe target follicular unit or other feature in the live still.

In one embodiment, a marker identification component 1804 is used toidentify markers in stills. The marker identification component may be,for example, a software program, or it may be embodied in hardware asmentioned above in reference to other components, that interacts withthe computer system 1810 including the processor 1807. This markeridentification component may also be a part of the signatureidentification component 1801.

In another embodiment, a system for tracking a feature of interestcomprises an interface, such as interface 1811, adapted to receive aplurality of images from an imaging device (which may not be included ina system itself), a signature identification component, such as 1801,and a marker referencing system 1805. The marker referencing system 1805receives information corresponding to a marker, follicular unit, orother feature identified by the signature identification component 1801in a first image and defines a frame of reference corresponding to themarker. It then determines whether the marker is in a second image, andadjusts the frame of reference corresponding to a change in position ofthe marker between images. The system may include data stored in memory,including a portable disk, a hard disk, flash memory, or any othermemory medium. It may also include a processor independent of processor1807, or it may use the same processor 1807.

The above components, including the signature identification component,the vectoring component, the tracking system, the marker identificationsystem, and the marker referencing system may be part of one software orhardware component, or may comprise multiple software and hardwareprograms and modules. Each module may be separable from any othermodule, or all the modules may be integral in one device or chip. Invarious embodiments and methods described herein, some of thesecomponents may be optional.

While one embodiment may include analysis of a body surface, the abovesystem and method may track any object and automatically update a frameor calculate a motion vector based on any object or set of objects.Objects that may be tracked include airborne objects, objects movingalong a surface, objects moving on the earth, or any other objects thatneed to be identified or tracked.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the claimedinvention. Those skilled in the art will readily recognize variousmodifications and changes that may be made to the claimed inventionwithout following the example embodiments and applications illustratedand described herein, and without departing from the true spirit andscope of the claimed invention, which is set forth in the followingclaims.

What is claimed is:
 1. A method for tracking a feature of interest on abody, the method comprising: determining a signature of a feature ofinterest in a first image of a body surface; determining pixel intensitydata of a region of interest in the first image; determining pixelintensity data of the region of interest in a second image of the bodysurface taken at a different point in time relative to the first image;splitting the first image into multiple first images; splitting thesecond image into multiple second images; comparing the pixel intensitydata of the region of interest in the first image with the pixelintensity data of the region of interest in the second image, whereincomparing comprises comparison of the each of the corresponding multipleof the first and second images to produce multiple predictions ofchanges in position of the feature of interest; and based on thecomparison of the pixel intensity data of the region of interest in thefirst and the second images and the signature of the feature ofinterest, locating the feature of interest in the second image.
 2. Themethod of claim 1, wherein determining the signature comprisesidentifying one or more characteristics from a following list: color,length, type, shape, and emergence angle.
 3. The method of claim 1,wherein the method comprises an optical flow algorithmic approach, aGray-Coded Bit-Plane technique, block search image stabilizationtechniques, or a combination of the above.
 4. The method of claim 1, themethod comprising using an image-acquisition device to capture imagesand moving the image-acquisition device to keep the feature of interestin a field of view of the image-acquisition device.
 5. The method ofclaim 1, further comprising determining whether at least one marker inthe first image is in the second image, wherein the at least one markercomprises a follicular unit, a mole, texturing, blood, a scar, afreckle, a wrinkle, a bump, or a depression of a body surface.
 6. Themethod of claim 1, wherein the feature of interest is a bald spot andthe method further comprises defining one or more hair implantationsites.
 7. The method of claim 1, further comprising determining a motionvector.
 8. A system for tracking a feature of interest on a body, thesystem comprising: an interface configured to receive a plurality ofimages of a body surface from an image acquisition device, at least afirst image comprising a feature of interest; a signature identificationcomponent, wherein the signature identification component identifiessignature information about the feature of interest in the first image;and a prediction component, wherein the prediction component comparespixel intensity data of a region of interest in the first image withpixel intensity data for the region of interest in a second image takenat a different point in time relative to the first image, and based onthe comparison of pixel intensity data predicts a change in position ofthe feature of interest from the first image to the second image tolocate the feature of interest in the second image; wherein theprediction component splits the first image into multiple first imagesand splits the second image into multiple second images, and whereincomparison of the pixel intensity data from the region of interest inthe first image and the second image comprises comparison of the each ofthe corresponding multiple of first and second images to producemultiple predictions of changes in position of the feature of interest.9. The system of claim 8, wherein one or both of the signatureidentification component and the prediction component comprises aprocessor connected to a memory and/or the image capture device.
 10. Thesystem of claim 8, further comprising a vectoring component, wherein thevectoring component is configured to calculate one or more motionvectors associated with movement of the body surface depicted in thefirst and the second images.
 11. The system of claim 10, wherein the oneor more motion vectors are calculated using a point cloud approach. 12.A method for tracking a feature of interest on a body, the methodcomprising: determining a signature of a feature of interest in a firstimage of a body surface; determining pixel intensity data of a region ofinterest in the first image; determining pixel intensity data of theregion of interest in a second image taken at a different point in timerelative to the first image; comparing the pixel intensity data of theregion of interest in the first image with the pixel intensity data forthe region of interest in the second image, splitting the first imageinto foreground and background sections, assigning each of theforeground and background sections a separate weight value, andutilizing the assigned weight values to predict the change in positionof the features of interest, and based on the comparison of pixel datain the region of interest in the first and the second images, thesignature of the feature of interest, and the predicted change inposition of the feature of interest, locating the feature of interest inthe second image.
 13. The method of claim 12, further comprising:splitting the first image into multiple first images; splitting thesecond image into multiple second images; and wherein comparing thepixel intensity data of the region of interest in the first image andthe second image comprises comparison of the each of the correspondingmultiple of the first and second images to produce multiple predictionsof changes in position of the feature of interest.
 14. The method ofclaim 12, further comprising: determining whether at least one marker inthe first image is in the second image; calculating a motion vectorcorresponding to a change in position of the at least one marker fromthe first image to the second image; and utilizing the motion vectorcorresponding to the change in position of the at least one marker fromthe first image to the second image to predict the change in position ofthe feature of interest.
 15. The method of claim 14, wherein identifyingthe marker comprises analyzing at least one of a length, an area, ashape, a type or a color of the marker.
 16. The method of claim 12,wherein predicting the change in position comprises predicting bothtranslational and rotational components of the feature of interest. 17.The method of claim 12, wherein the signature of the feature of interestis determined by transforming the first still image to extract one ormore characteristics that constitutes the signature of the feature ofinterest.
 18. The method of claim 12, wherein determining the signaturecomprises identifying one or more characteristics from a following list:color, length, type, shape, and emergence angle.
 19. The method of claim12, wherein the feature of interest is a bald spot and the methodfurther comprises defining one or more hair implantation sites.
 20. Asystem for tracking a feature of interest on a body, the systemcomprising: an interface configured to receive from an image capturedevice a plurality of images of a body surface, at least a first imagecomprising a feature of interest; a signature identification component,wherein the signature identification component identifies signatureinformation about the feature of interest in the first image; and aprediction component, wherein the prediction component compares pixelintensity data of a region of interest in the first image with pixelintensity data for the region of interest in a second image taken at adifferent point in time relative to the first image, splits the firstimage into foreground and background sections, assigns each of theforeground and background sections a separate weight value, and based onthe comparison of pixel intensity data, the signature of the feature ofinterest and the assigned weight values predicts a change in position ofthe feature of interest from the first image to the second image tolocate the feature of interest in the second image.
 21. The system ofclaim 20, wherein the prediction component comprises an algorithm thatimplements one or more approaches from a following list: a gray-codedbit plane approach, optical flow approach, block search imagestabilization approach, or a combination of the above.
 22. The system ofclaim 20, wherein the signature identification component and theprediction component are part of a single software or hardware product.23. The system of claim 20, wherein one or both of the signatureidentification component and the prediction component comprises aprocessor connected to a memory and/or the image capture device.
 24. Thesystem of claim 23, wherein the processor associates an electronicidentifier with at least one marker and stores the electronic identifierin the memory.
 25. The system of claim 20, wherein the system is arobotic system comprising a robotic arm and at least one cameraoperatively connected to the robotic arm.